AIGP Prüfungsfragen Prüfungsvorbereitungen 2025: IAPP Certified Artificial Intelligence Governance Professional - Zertifizierungsprüfung IAPP AIGP in Deutsch Englisch pdf downloaden

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Thema	Einzelheiten
Thema 1	Understanding the Foundations of AI Governance: This section of the exam measures skills of AI governance professionals and covers the core concepts of AI governance, including what AI is, why governance is needed, and the risks and unique characteristics associated with AI. It also addresses the establishment and communication of organizational expectations for AI governance, such as defining roles, fostering cross-functional collaboration, and delivering training on AI strategies. Additionally, it focuses on developing policies and procedures that ensure oversight and accountability throughout the AI lifecycle, including managing third-party risks and updating privacy and security practices.
Thema 2	Understanding How Laws, Standards, and Frameworks Apply to AI: This section of the exam measures skills of compliance officers and covers the application of existing and emerging legal requirements to AI systems. It explores how data privacy laws, intellectual property, non-discrimination, consumer protection, and product liability laws impact AI. The domain also examines the main elements of the EU AI Act, such as risk classification and requirements for different AI risk levels, as well as enforcement mechanisms. Furthermore, it addresses the key industry standards and frameworks, including OECD principles, NIST AI Risk Management Framework, and ISO AI standards, guiding organizations in trustworthy and compliant AI implementation.
Thema 3	Understanding How to Govern AI Deployment and Use: This section of the exam measures skills of technology deployment leads and covers the responsibilities associated with selecting, deploying, and using AI models in a responsible manner. It includes evaluating key factors and risks before deployment, understanding different model types and deployment options, and ensuring ongoing monitoring and maintenance. The domain applies to both proprietary and third-party AI models, emphasizing the importance of transparency, ethical considerations, and continuous oversight throughout the model’s operational life.
Thema 4	Understanding How to Govern AI Development: This section of the exam measures the skills of AI project managers and covers the governance responsibilities involved in designing, building, training, testing, and maintaining AI models. It emphasizes defining the business context, performing impact assessments, applying relevant laws and best practices, and managing risks during model development. The domain also includes establishing data governance for training and testing, ensuring data quality and provenance, and documenting processes for compliance. Additionally, it focuses on preparing models for release, continuous monitoring, maintenance, incident management, and transparent disclosures to stakeholders.

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IAPP Certified Artificial Intelligence Governance Professional AIGP Prüfungsfragen mit Lösungen (Q164-Q169):

164. Frage
What is the most important factor when deciding whether or not to select a proprietary AI model?

A. Whether its system card identifies risks.
B. Whether its training data is disclosed.
C. What business purpose it will serve.
D. How frequently it will be updated.

Antwort: C

Begründung:
Theprimary considerationin selecting any AI system, especially aproprietary model, is itsfit for business purpose. Whether it serves the intended goals is foundational before evaluating technical or governance features.
From theAI Governance in Practice Report 2024:
"AI governance starts with defining the corporate strategy for AI... and aligning systems with business purpose and operational context." (p. 11)
* B, C, Dare relevant for evaluation, but onlyafterconfirming business applicability.

165. Frage
To maintain fairness in a deployed system, it is most important to?

A. Protect against loss of personal data in the model.
B. Detect anomalies outside established metrics that require new training data.
C. Optimize computational resources and data to ensure efficiency and scalability.
D. Monitor for data drift that may affect performance and accuracy.

Antwort: D

Begründung:
To maintain fairness in a deployed system, it is crucial to monitor for data drift that may affect performance and accuracy. Data drift occurs when the statistical properties of the input data change over time, which can lead to a decline in model performance. Continuous monitoring and updating of the model with new data ensure that it remains fair and accurate, adapting to any changes in the data distribution. Reference: AIGP Body of Knowledge on Post-Deployment Monitoring and Model Maintenance.

166. Frage
Which of the following use cases would be best served by a non-AI solution?

A. A non-profit wants to develop a social media presence.
OB. An e-commerce provider wants to make personalized recommendations.
B. A business analyst wants to forecast future cost overruns and underruns.
C. A customer service agency wants automate answers to common questions.

Antwort: A

Begründung:
Developing a social media presence for a non-profit is best served by non-AI solutions. This task primarily involves content creation, community engagement, and strategic planning, which are effectively managed by human expertise and traditional marketing tools. AI is more suitable for tasks requiring automation, large-scale data analysis, and personalized recommendations, such as e-commerce personalization, forecasting cost overruns, or automating customer service responses. Reference: AIGP Body of Knowledge on AI Use Cases and Applications.

167. Frage
Machine learning is best described as a type of algorithm by which?

A. Systems can mimic human intelligence with the goal of replacing humans.
B. Previously unknown properties are discovered in data and used to predict and make improvements in the data.
C. Statistical inferences are drawn from a sample with the goal of predicting human intelligence.
D. Systems can automatically improve from experience through predictive patterns.

Antwort: D

Begründung:
Machine learning (ML) is a subset of artificial intelligence (AI) where systems use data to learn and improve over time without being explicitly programmed. Option B accurately describes machine learning by stating that systems can automatically improve from experience through predictive patterns. This aligns with the fundamental concept of ML where algorithms analyze data, recognize patterns, and make decisions with minimal human intervention. Reference: AIGP BODY OF KNOWLEDGE, which covers the basics of AI and machine learning concepts.

168. Frage
What is the primary reason the EU is considering updates to its Product Liability Directive?

A. Address digital services and connected products.
B. To define new liability exemptions for defective products.
C. Address free and open-source software.
D. To increase the minimum warranty level for defective goods.

Antwort: A

Begründung:
The primary reason the EU is considering updates to its Product Liability Directive is to address digital services and connected products. The current directive does not adequately cover the complexities and challenges posed by modern digital and connected technologies. By updating the directive, the EU aims to ensure that it remains relevant and effective in addressing the liabilities associated with these advanced products, ensuring consumer protection and fair market practices in the digital age.

169. Frage
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AIGP Prüfungsfragen Prüfungsvorbereitungen 2025: IAPP Certified Artificial Intelligence Governance Professional - Zertifizierungsprüfung IAPP AIGP in Deutsch Englisch pdf downloaden

IAPP AIGP Prüfungsplan:

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IAPP Certified Artificial Intelligence Governance Professional AIGP Prüfungsfragen mit Lösungen (Q164-Q169):

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